Multi-Level Clustering-Based Outlier’s Detection (MCOD) Using Self-Organizing Maps

Li, Menglu; Kashef, Rasha; Ibrahim, A.

doi:10.3390/bdcc4040024

Cited by 17 publications

(4 citation statements)

References 23 publications

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“…Munoz and Muruzábal used SOMs for outlier detection in distorted characters and in milk container data [8]. Li et al used them for outlier detection in biomedical and credit card applications [9]. Shahreza used a method combining a SOM with particle swarm optimization for anomaly detection applied to forest fire detection [10].…”

Section: Methods a Som Distancementioning

confidence: 99%

Robust Semi-Supervised Classification using GANs with Self-Organizing Maps

Fick¹,

Gader²,

Zare³

2021

Preprint

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Generative adversarial networks (GANs) have shown tremendous promise in learning to generate data and effective at aiding semi-supervised classification. However, to this point, semi-supervised GAN methods make the assumption that the unlabeled data set contains only samples of the joint distribution of the classes of interest, referred to as inliers. Consequently, when presented with a sample from other distributions, referred to as outliers, GANs perform poorly at determining that it is not qualified to make a decision on the sample. The problem of discriminating outliers from inliers while maintaining classification accuracy is referred to here as the DOIC problem. In this work, we describe an architecture that combines self-organizing maps (SOMs) with SS-GANS with the goal of mitigating the DOIC problem and experimental results indicating that the architecture achieves the goal. Multiple experiments were conducted on hyperspectral image data sets. The SS-GANS performed slightly better than supervised GANS on classification problems with and without the SOM. Incorporating the SOMs into the SS-GANs and the supervised GANS led to substantially mitigation of the DOIC problem when compared to SS-GANS and GANs without the SOMs. Furthermore, the SS-GANS performed much better than GANS on the DOIC problem, even without the SOMs.

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Section: Methods a Som Distancementioning

confidence: 99%

Robust Semi-Supervised Classification using GANs with Self-Organizing Maps

Fick¹,

Gader²,

Zare³

2021

Preprint

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“…Here, t is the iteration number, η(t) is the learning rate at iteration t , and h(c i , t) is the neighbourhood function that determines the influence of the input vector x i on the weight vector w i based on the distance between c i and the winning node, and c i is the index of the winning node in the grid for the input vector x i [ 68 , 69 , 70 ].…”

Section: Clustering Analysismentioning

confidence: 99%

Automatic Post-Stroke Severity Assessment Using Novel Unsupervised Consensus Learning for Wearable and Camera-Based Sensor Datasets

Razfar

Kashef

Mohammadi

2023

Sensors

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Stroke survivors often suffer from movement impairments that significantly affect their daily activities. The advancements in sensor technology and IoT have provided opportunities to automate the assessment and rehabilitation process for stroke survivors. This paper aims to provide a smart post-stroke severity assessment using AI-driven models. With the absence of labelled data and expert assessment, there is a research gap in providing virtual assessment, especially for unlabeled data. Inspired by the advances in consensus learning, in this paper, we propose a consensus clustering algorithm, PSA-NMF, that combines various clusterings into one united clustering, i.e., cluster consensus, to produce more stable and robust results compared to individual clustering. This paper is the first to investigate severity level using unsupervised learning and trunk displacement features in the frequency domain for post-stroke smart assessment. Two different methods of data collection from the U-limb datasets—the camera-based method (Vicon) and wearable sensor-based technology (Xsens)—were used. The trunk displacement method labelled each cluster based on the compensatory movements that stroke survivors employed for their daily activities. The proposed method uses the position and acceleration data in the frequency domain. Experimental results have demonstrated that the proposed clustering method that uses the post-stroke assessment approach increased the evaluation metrics such as accuracy and F-score. These findings can lead to a more effective and automated stroke rehabilitation process that is suitable for clinical settings, thus improving the quality of life for stroke survivors.

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“…Precision, Recall, F1 score, and Accuracy [49][50][51][52][53][54] the well-known evaluation metrics to assess the performance of a classifier. Precision finds pertinent instances among the gathered instances.…”

Section: Evaluation Metricsmentioning

confidence: 99%

E2DR: A Deep Learning Ensemble-Based Driver Distraction Detection with Recommendations Model

Aljasim

Kashef

2022

Sensors

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The increasing number of car accidents is a significant issue in current transportation systems. According to the World Health Organization (WHO), road accidents are the eighth highest top cause of death around the world. More than 80% of road accidents are caused by distracted driving, such as using a mobile phone, talking to passengers, and smoking. A lot of efforts have been made to tackle the problem of driver distraction; however, no optimal solution is provided. A practical approach to solving this problem is implementing quantitative measures for driver activities and designing a classification system that detects distracting actions. In this paper, we have implemented a portfolio of various ensemble deep learning models that have been proven to efficiently classify driver distracted actions and provide an in-car recommendation to minimize the level of distractions and increase in-car awareness for improved safety. This paper proposes E2DR, a new scalable model that uses stacking ensemble methods to combine two or more deep learning models to improve accuracy, enhance generalization, and reduce overfitting, with real-time recommendations. The highest performing E2DR variant, which included the ResNet50 and VGG16 models, achieved a test accuracy of 92% as applied to state-of-the-art datasets, including the State Farm Distracted Drivers dataset, using novel data splitting strategies.

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Multi-Level Clustering-Based Outlier’s Detection (MCOD) Using Self-Organizing Maps

Cited by 17 publications

References 23 publications

Robust Semi-Supervised Classification using GANs with Self-Organizing Maps

Robust Semi-Supervised Classification using GANs with Self-Organizing Maps

Automatic Post-Stroke Severity Assessment Using Novel Unsupervised Consensus Learning for Wearable and Camera-Based Sensor Datasets

E2DR: A Deep Learning Ensemble-Based Driver Distraction Detection with Recommendations Model

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